Dual Attention Networks for Few-Shot Fine-Grained Recognition

Abstract

The task of few-shot fine-grained recognition is to classify images belonging to subordinate categories merely depending on few examples. Due to the fine-grained nature, it is desirable to capture subtle but discriminative part-level patterns from limited training data, which makes it a challenging problem. In this paper, to generate fine-grained tailored representations for few-shot recognition, we propose a Dual Attention Network (Dual Att-Net) consisting of two dual branches of both hard- and soft-attentions. Specifically, by producing attention guidance from deep activations of input images, our hard-attention is realized by keeping a few useful deep descriptors and forming them as a bag of multi-instance learning. Since these deep descriptors could correspond to objects' parts, the advantage of modeling as a multi-instance bag is able to exploit inherent correlation of these fine-grained parts. On the other side, a soft attended activation representation can be obtained by applying attention guidance upon original activations, which brings comprehensive attention information as the counterpart of hard-attention. After that, both outputs of dual branches are aggregated as a holistic image embedding w.r.t. input images. By performing meta-learning, we can learn a powerful image embedding in such a metric space to generalize to novel classes. Experiments on three popular fine-grained benchmark datasets show that our Dual Att-Net obviously outperforms other existing state-of-the-art methods.

Graph Neural Networks With Triple Attention for Few-Shot Learning

“…CubMeta [43] introduced the concept of curriculum learning into meta-learning and proposed an effective self-paced meta-learning method to obtain stronger meta-learners for few-shot classifications. DUAL ATT-NET [47] adopted a dual-attention to explicitly model the crucial relation of fine-grained parts and implicitly captures discriminative while subtle fine-grained details. While these methods are all based on CNNs for feature embedding, most recent works exploited GNN for more effective modeling of inter-and intra-class relations in few-shot classification.…”

“…Attention Mechanism [50] aims to focus on image regions that are more task-related by learning a binary matrix or a weighted matrix. In particular, self-attention [47], [51]- [53] considers the inherent correlation (attention) of the input features itself, which is mostly applied in deep models. In GCN scenarios, GAT [54] used a graph attention layer to learn a weighted parameter vector based on entire neighborhoods to update node representation.…”

Feature fusion network based on few-shot fine-grained classification

“…We reported the average accuracy (%) for 600 randomly generated episodes, along with the 95% confidence interval on the test set, following the approach commonly used in most methods (Jankowski et al, 2011;Nichol et al, 2018;Xu et al, 2022). Our model was trained end-to-end, without any pre-training process.…”

Research on the Fusion of Artificial Intelligence and 6G in Computer Network Technology

“…These methods learn an initialization that can be fine-tuned quickly on a new task with a small amount of data. Attention-based methods, e.g., MultiAtt [33], MattML [46] and Dual Att-Net [41], use attention mechanisms to identify the most informative parts or regions of the input images. These methods aim to learn a feature representation that is not only discriminative but also informative for few-shot recognition.…”